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Conference
publications |
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M. M. Komarnicki, M. W. Przewozniczek, R. Tinós, and X. Li, Overlapping Cooperative
Co-Evolution for Overlapping Large-Scale Global Optimization Problems, In
Proceedings of the Genetic and
Evolutionary Computation Conference (GECCO '24), pp. 665–673, 2024. |
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L. Tulczyjew, M. W. Przewozniczek, R. Tinós, A. M. Wijata, and J.
Nalepa, CANNIBAL
Unveils the Hidden Gems: Hyperspectral Band Selection via Clustering of
Weighted Variable Interaction Graphs, In Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO '23), pp. 412–421, 2024. |
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R. Tinós, M. W. Przewozniczek, D.
Whitley, F. Chicano, Genetic Algorithm
with Linkage Learning, In Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO '23), pp.
981-989, ACM, 2023 . |
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M. W. Przewozniczek,
M. M. Komarnicki, To slide or not to
slide? Moving along fitness levels and preserving the gene subsets diversity
in modern evolutionary algorithms, In Proceedings
of the Genetic and Evolutionary Computation Conference (GECCO '23), pp.
955-962, ACM, 2023. |
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M. M. Komarnicki, M. W. Przewozniczek, H. Kwasnicka, K.
Walkowiak, Incremental
Recursive Ranking Grouping - A Decomposition Strategy for Additively and Nonadditively Separable Problems, In Proceedings of the Genetic and
Evolutionary Computation Conference Companion (GECCO '23), pp. 27-28,
ACM, 2023. |
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M. W. Przewozniczek, R. Tinós, B. Frej, M. M. Komarnicki, On turning Black-
into Dark Gray-optimization with the Direct
Empirical Linkage Discovery and Partition Crossover, In Proceedings of the Genetic and
Evolutionary Computation Conference (GECCO '22), pp. 269–277 ACM, 2022. |
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M. W. Przewozniczek, M. M.
Komarnicki, "Empirical linkage learning for non-binary
discrete search spaces in the optimization of a large-scale real-world
problem," in Proceedings of the 2022 Genetic and Evolutionary Computation
Conference Companion (GECCO '22), ACM, 2022. (in press) |
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M. W. Przewozniczek,
M. M. Komarnicki, P. A. N. Bosman, D. Thierens, B. Frej, N. H. Luong,
Hybrid Linkage Learning
for Permutation Optimization with Gene-pool Optimal Mixing Evolutionary
Algorithms, in Proceedings of the
2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21),
ACM, pp. 1442–1450, 2021. |
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M.W. Przewozniczek,
M. M. Komarnicki, B. Frej,
Direct linkage
discovery with empirical linkage learning, in Proceedings of the 2021 Genetic and Evolutionary Computation
Conference (GECCO ’21), ACM, pp. 609–617, 2021. |
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M. W. Przewozniczek, P. Dziurzanski,
S. Zhao, L. S. Indrusiak, Multi-Objective
Parameter-less Population Pyramid in Solving the Real-World and Theoretical
Problems, in Proceedings of the
2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21).
ACM,
pp. 41-42, 2021. |
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M. W. Przewozniczek, M. M. Komarnicki, Fitness caching - from a minor mechanism to major consequences in
modern evolutionary computation, in Proceedings
of the IEEE Congress on Evolutionary Computation (CEC), pp. 1785-1791, 2021. |
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M. W. Przewozniczek, B. Frej, M. M. Komarnicki, On measuring and
improving the quality of linkage learning in modern evolutionary algorithms
applied to solve partially additively separable problems, in Proceedings of the 2020 Genetic and
Evolutionary Computation Conference (GECCO ’20). ACM, pp. 742–750, 2020. |
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M. M. Komarnicki, M. W. Przewozniczek,
T. Durda, Comparative Mixing for
DSMGA-II, in Proceedings of the
2020 Genetic and Evolutionary Computation Conference (GECCO ’20), ACM,
pp. 708–716, 2020. |
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S. Wozniak, M. W. Przewozniczek,
and M. M. Komarnicki, Parameter-less
population pyramid for permutation-based problems, in Proceedings of the Parallel Problem
Solving from Nature (PPSN XVI), pp. 418-430, 2020. |
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S. Zhao P. Dziurzanski, M. W. Przewozniczek,
M. Komarnicki, L.S. Indrusiak,
Cloud-based Dynamic
Distributed Optimisation of Integrated Process Planning and Scheduling in
Smart Factories, Proceedings of the
Genetic and Evolutionary Computation Conference, (GECCO 19), pp.
1381-1389, 2019. |
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A.M. Zieliński, M. M. Komarnicki
and M. W. Przewozniczek, Parameter-less population
pyramid with automatic feedback, Proceedings
of the Genetic and Evolutionary Computation Conference Companion, (GECCO 19),
pp. 312-313, 2019. |
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S. Zhao, H. Mei,
P. Dziurzanski, M. W. Przewozniczek,
L.S. Indrusiak, Cloud-Based
Integrated Process Planning and Scheduling Optimisation via Asynchronous
Islands, in: Djemame K., Altmann J.,
Bañares J., Agmon
Ben-Yehuda O., Naldi M. (eds)
Economics of Grids, Clouds, Systems, and Services. GECON 2019. Lecture Notes
in Computer Science, Vol. 11819. Springer, Cham, pp. 247-259, 2019. |
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M. W. Przewozniczek, M. M. Komarnicki,
The
practical use of problem encoding allowing cheap fitness computation of
mutated individuals, Proceedings of
the 2018 Federated Conference on Computer Science and Information Systems, (FedCSIS 2019), pp. 57-65, 2018. |
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M. W. Przewozniczek,
M. M. Komarnicki, The influence of
fitness caching on modern evolutionary methods and fair computation load
measurement, Proceedings of the
Genetic and Evolutionary Computation Conference Companion, (GECCO 18), pp.
241-242, 2018. |
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M. Przewozniczek, Problem encoding allowing
cheap fitness computation of mutated individuals, Proceedings of the IEEE Congress on Evolutionary Computation (CEC),
pp.308-316, 2017. |
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M. M. Komarnicki
M. W. Przewozniczek, Parameter-less
population pyramid with feedback, Proceedings
of the Genetic and Evolutionary Computation Conference Companion, (GECCO 17),
pp. 109–110, 2017. |
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M. W. Przewozniczek,
K. Walkowiak, M. Aibin, The
Effectiveness of the Simplicity in Evolutionary Computation, Intelligent Information and Database
Systems: 9th Asian Conference, ACIIDS 2017, pp. 392–402, 2017. |
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M. M. Komarnicki, M. W. Przewozniczek,
Linked Genes
Migration in Island Models, Proceedings
of the 8th International Joint Conference on Computational Intelligence,
IJCCI 2016, vol. 3, pp. 30-40, 2016. |
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M. Przewozniczek, Dynamic Subpopulation
Number Control for Solving Routing and Spectrum Allocation Problems in
Elastic Optical Networks, Proceedings
of the Third European Network Intelligence Conference, ENIC, pp.257-264,
2016. |
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M. Przewozniczek, Multi
population pattern searching algorithm for solving routing spectrum
allocation with joint unicast and anycast problem
in elastic optical networks, Proceedings
of the 16th International Conference on Intelligent data engineering and
automated learning - IDEAL 2015, Vol. 42, No. 21, pp.328-339, 2015. |
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B. Fidrysiak, M.
W. Przewozniczek, Towards finding an
effective way of discrete problems solving: the particle swarm optimization,
genetic algorithm and linkage learning techniques hybrydization,
Proceedings of the 7th International
Joint Conference on Computational Intelligence, IJCCI 2015, vol. 1, pp.
228-236, 2015. |
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M. Przewozniczek, Towards finding an
effective uniform and single point crossover balance for optimization of
Elastic Optical Networks, Proceedings
of The Second European Network Intelligence Conference, ENIC 2015,
pp.40-46, 2015. |
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M. Przewoźniczek,
K. Walkowiak, Quasi-hierarchical
Evolutionary Algorithm for Flow Optimization Survivable MPLS Networks, Proceedings of the 7th International
Conference on Computational Science and Its Applications, ICCSA 2007, Lecture
Notes in Computer Science, Vol. 4707, Springer Verlag,
2007, s. 330-342, 2007. |
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